Chipmaker gets the $750,000 it needs to build a tiny new computing platform.

A month ago, we told you about a chipmaker called Adapteva that turned to Kickstarter in a bid to build a new platform that would be the size of a Raspberry Pi and an alternative to expensive parallel computing platforms. Adapteva needed at least $750,000 to build what it is calling "Parallella"—and it has hit the goal.

Today is the Kickstarter deadline, and the project is up to more than $830,000 with a few hours to go. (UPDATE: The fundraiser hit $898,921 when time expired.) As a result, Adapteva will build 16-core boards capable of 26 gigaflops performance, costing $99 each. The board uses RISC cores capable of speeds of 1GHz each. There is also a dual-core ARM A9-based system-on-chip, with the 16-core RISC chips acting as a coprocessor to speed up tasks.

Adapteva is well short of its stretch goal of $3 million, which would have resulted in a 64-core board hitting 90 gigaflops, and built using a more expensive 28-nanometer process rather than the 65-nanometer process used for the base model. The 64-core board would have cost $199.

4,965 people backed the project. The vast majority of them pledged at least $99, meaning they'll receive one of the 16-core boards, which are scheduled to ship between February and May 2013.

Adapteva is calling Parallella a "supercomputer," although the vaguely-defined term is usually applied to the types of large clusters used by government labs and research organizations, rather than computers than can fit right under your monitor. Parallella boards can be clustered together to hit higher levels of performance. Alternatively, the board could be used for simpler tasks, like turning your TV into a home computer. But Adapteva's main target for initial sales, CEO and founder Andreas Olofsson told us last month when the Kickstarter went live, are hobbyists and developers, "the guy who is working on an open source project and there’s no platform they can use today that fits their needs."

There's this little thing called parallel computing, which generally uses large computing clusters to analyse data or make mass calculations, and therefor greatly benefits from cheaper hardware. This practice has many uses such as... performing research to cure cancer.

We don't really neeeeeed to cure cancer, but it would be kinda nice.

There are radically higher-performance parallel processing platforms already available off the shelf, to regular developers and hobbyists. A $99 discrete GPU has dramatically higher throughput. Just the 4 Cortex A9's in any quad core phone SoC can match the claimed theoretical 26 GFLOPs.

All of this is not to say that those are ideal parallel computing platforms for any particular workload, either. But they surpass the Parallella offering on nearly every axis, they are already available off the shelf by the million, and they are well supported by many existing operating systems and toolchains.

Most concerning, though, is that the Adapteva claims almost entirely fail to address the actually hard part of a high-performance parallel architecture: the memory system. Jamming a pile of general-purpose scalar cores on a die with a grid topology is the easy part. Just die shrinking the original MIT RAW prototype design to 65nm would give a very similar device, now a decade later. And existing designs have shown that grid topologies aren't even necessarily a good idea; RAW/Tilera argue in their favor, Intel, NVIDIA, and AMD all strongly argue against, with Xeon Phi née Larrabee's ring of rings shown to be a strong choice at the dozens-to-hundreds of cores scale both in area overhead and latency. But regardless, how do they expect a few dozen, let alone hundreds or thousands of independent cores pounding on their own little subproblems to saturate a wide DRAM interface, where peak bandwidth is only reached by few, extremely wide transactions to even fewer memory pages? I don't mean to imply that it's not possible, just that their "virtual ghz" multipliers, and total lack of discussion of the memory system or of how they expect to do better than the mass of related architectures and prior work, don't suggest that this is likely a grand solution to the challenge of building an efficient parallel architecture, or even competitive with existing commodity hardware in the same price (GeForce GT 640) and power (Tegra 3, Snapdragon S4) range.

Yup I love to see a project like this succeed. I don't think anyone involved in HPC is going to get confused as to the suitability of this product. The people who bought this, are of the same mindset as the people who built it.